In many industrial applications, neither a first-principle model nor a purely data-driven model is constructed with high accuracy due to various reasons including unknown environmental conditions, a varying composition of raw materials, knowledge caveats, imprecise data registration, and a lack of exploration. In addition, there may be parts of the system for which only a few measurements are available due to a high cost or inability to conduct more. What complicates matters further is that these measurements may have irregular sampling rates because smart sampling algorithms (see [1]}) have been applied or measurements have been conducted manually. To compose accurate models, one is often forced to combine both first-principle information and measurement data. These resulting grey-box models come in many different forms [2]}. In an attempt to improve the model accuracy while keeping the model complexity low, we can use an artificial neural network to model only those parts of the system for which no first-principle model is available or for which the solution of the first-principle model is difficult to compute or inaccurate. However, due to a high network complexity, this alone is generally not enough to ensure that the resulting model is simple enough to be used for real-time applications.
